An integrated transmission method for uplink communication and downlink sensing

Abstract

This invention discloses an integrated uplink communication and downlink sensing transmission method. This method is designed for Time Division Duplex (TDD) Multiple Input Multiple Output (MIMO) Sensor-Integrated Communication (ISAC) systems, considering the mutual interference between uplink communication and downlink radar sensing. Under the premise of limited downlink and uplink transmit power, the method minimizes the weighted sum of the mean square errors of sensing and communication by jointly designing the downlink sensing waveform and uplink communication power allocation. Compared with traditional methods that design communication and sensing independently, this invention effectively reduces the interference between uplink communication and radar sensing, significantly improves communication and sensing performance, and adds almost no computational complexity, making it easy to implement in engineering.

Description

Technical Field

[0001] This invention belongs to the field of integrated communication and sensing systems, and particularly relates to an integrated transmission method for uplink communication and downlink sensing. Background Technology

[0002] The Internet of Things (IoT) has brought about new applications that integrate communication and sensing. Traditional systems that separate communication and sensing are inefficient in utilizing time and frequency resources, suffer from severe interference, and are costly, making them unsuitable for these new applications. Therefore, integrated communication and sensing technology has become an efficient and feasible method to meet these new needs. In integrated communication and sensing technology, communication and sensing share hardware resources, performing communication and radar sensing simultaneously on the same frequency. This effectively saves time and frequency resources and hardware costs, resulting in higher system efficiency.

[0003] In time-division multiplexing integrated sensing systems, in order to ensure transmission efficiency, the guard interval between the downlink and uplink phases is usually very short, which makes the collision between the uplink signal and the target echo inevitable, causing serious interference problems. Summary of the Invention

[0004] The purpose of this invention is to provide an integrated transmission method for uplink communication and downlink sensing, which can minimize the error of sensing estimation and communication data detection by jointly optimizing the downlink sensing waveform and uplink power allocation when there is partial collision between the uplink signal and the target echo.

[0005] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0006] Includes the following steps:

[0007] An integrated uplink communication and downlink sensing transmission method includes the following steps:

[0008] Step 1: Set the upper limit and weighting coefficient for the downlink sensing power of the base station and the uplink transmission power of the user respectively;

[0009] Step 2: Based on the linear minimum mean square error (LMMSE) receiver for sensing parameter estimation and uplink communication data detection, obtain the mean square error for sensing estimation and communication data detection, respectively.

[0010] Step 3: Based on the weighting coefficients, under the constraints of base station downlink sensing and user uplink transmit power, optimize the base station downlink sensing waveform and user uplink transmit power allocation with the goal of minimizing the weighted sum of the mean square error of sensing estimation and communication data detection.

[0011] Furthermore, in step 2, the sensing parameters and uplink communication data are estimated using the following LMMSE receivers:

[0012] Sensing receiver:

[0013]

[0014] Uplink communication receiver:

[0015]

[0016] Where Ψ represents the sensing receiver, W l Indicates the uplink communication receiver for time slot l, (·) H This indicates the conjugate transpose, (·) T This represents the matrix transpose, (·). -1 This represents finding the inverse of a matrix. Let R represent the Kronecker product of matrices A and B. g Let S be the covariance matrix of the target response vector g, and let S be the downlink sensing waveform. Let S represent the equivalent form, and H be the user's uplink communication channel. Indicates dimension L d The identity matrix, Indicates a dimension of N r L d The identity matrix, / refers to the meaning of and . Let the dimension be N d The identity matrix, This indicates the uplink signal in the overlapping part of downlink sensing / uplink communication. The variance matrix, It is the variance of the additive white Gaussian noise at the base station receiving antenna array. This is the user uplink power allocation matrix corresponding to the overlap phase. This is the user uplink power allocation matrix for the pure uplink communication phase, where diag(a) represents a diagonal matrix with elements of vector a as its diagonal elements, K represents the number of uplink users, and p k p′ represents the uplink transmit power of user k during the downlink sensing / uplink communication overlap phase. k s represents the uplink transmit power of user k during the pure uplink communication phase. l It is the sensing waveform of time slot l. s l The equivalent form of L d It is the time slot length of the sensed waveform, L u L is the time slot length of the uplink data. o It is the time slot length during the downlink sensing / uplink communication overlap phase.

[0017] Furthermore, in step 3, based on the mean square error obtained in step 2, the following optimization problem is constructed to optimize the downlink sensing waveform S, the user uplink communication power allocation matrix P, and P′:

[0018] The optimization objective is to minimize the weighted sum of mean squared errors.

[0019]

[0020] The constraints are:

[0021]

[0022] 0≤p k ≤P k k = 1, 2, ..., K

[0023] 0≤p′ k ≤P k k = 1, 2, ..., K

[0024] Where ρ represents the weighting coefficient, tr(·) is the trace of the matrix, and ||·||2 is the 2-norm of the vector. For x u1 The covariance matrix, For the echo phase, the equivalent uplink data vector is vec(·), which denotes vectorization. l P is the uplink data vector for time slot l. b P represents the base station transmit power budget. k This represents the uplink transmit power budget for user k.

[0025] Furthermore, in step 3, the original optimization problem is transformed into three sub-optimization problems using the controlled minimization MM algorithm:

[0026] Subproblem 1: With a fixed power allocation matrix P, solve for the downlink sensing waveform S.

[0027] The optimization objective is to minimize

[0028]

[0029] The constraints are:

[0030] in, and

[0031] As an intermediate variable, The solution obtained in the t-th iteration The value,

[0032] The solution obtained in the t-th iteration The value;

[0033] Subproblem 2: Given a fixed downlink sensing waveform S, solve for the power allocation matrix P.

[0034] The optimization objective is to minimize

[0035]

[0036] The constraints are:

[0037] 0≤p k ≤P k k = 1, 2, ..., K

[0038] in, and It is an intermediate variable used to simplify formulas. The solution obtained in the t-th iteration The value of P (t) The value of P obtained from the t-th iteration;

[0039] Subproblem 3: Solving for the power allocation matrix P′

[0040] The optimization objective is to minimize

[0041]

[0042] The constraints are:

[0043] 0≤p′ k ≤P k k = 1, 2, ..., K

[0044] in, P is an intermediate variable used to simplify the formula. ′(t) Let P' be the value obtained from the t-th iteration.

[0045] Furthermore, in step 3, sub-optimization problem 1 and sub-optimization problem 2 are solved alternately, and the optimal solutions for the downlink sensing waveform S and the power allocation matrix P are obtained when the iteration converges; the optimal solution for the power allocation matrix P′ is obtained by iteratively solving sub-problem 3.

[0046] The integrated uplink communication and downlink sensing transmission method of the present invention has the following advantages:

[0047] 1. This invention addresses the situation where the uplink signal and target echo partially collide in an integrated sensing system. By jointly designing the downlink sensing waveform and the uplink multi-user power allocation scheme, the weighted sum of the mean square error of sensing estimation and uplink communication data detection is minimized under the constraint of transmit power.

[0048] 2. Compared with the method of designing uplink communication and sensing independently, the present invention effectively reduces the interference between uplink communication and radar sensing, and reduces the error in sensing parameter estimation and communication data detection.

[0049] 3. The computational complexity of this invention is comparable to that of the uplink communication and sensing independent design method, which is conducive to engineering implementation. Attached Figure Description

[0050] Figure 1 This is the algorithm flowchart of the present invention.

[0051] Figure 2 This is a diagram showing the results of a simulation experiment. Detailed Implementation

[0052] To better understand the purpose, structure, and function of this invention, the following detailed description of an integrated uplink communication and downlink sensing transmission method is provided in conjunction with the accompanying drawings.

[0053] A typical application scenario for this invention is an uplink multi-user MIMO system where radar echoes and uplink signals partially overlap. By jointly designing the downlink sensing waveform and the uplink multi-user transmit power allocation scheme, the weighted sum of the mean square errors of sensing estimation and uplink communication data detection is minimized within the transmit power budget constraint. Figure 1 As shown in the figure, the integrated uplink communication and downlink sensing transmission method disclosed in this embodiment of the invention has the following specific steps:

[0054] (1) Set the upper limit and weighting coefficient of the downlink sensing power of the base station and the uplink transmission power of the user respectively;

[0055] (2) Based on the linear minimum mean square error (LMMSE) receiver for sensing parameter estimation and uplink communication data detection, the mean square errors of sensing estimation and communication data detection are obtained respectively.

[0056] The LMMSE receiver obtained in this step is:

[0057] Sensing receiver:

[0058]

[0059] Uplink communication receiver:

[0060]

[0061] Where Ψ represents the sensing receiver, W l Indicates the uplink communication receiver for time slot l, (·) H This indicates the conjugate transpose, (·) T To represent the matrix transpose, (·)- 1 This represents finding the inverse of a matrix. Let R represent the Kronecker product of matrices A and B. g Let S be the covariance matrix of the target response vector g, and let S be the downlink sensing waveform. Let S represent the equivalent form, and H be the user's uplink communication channel. Indicates dimension L d The identity matrix, Indicates a dimension of N r L d The identity matrix, Let the dimension be N d The identity matrix, This indicates the uplink signal in the overlapping part of downlink sensing / uplink communication. The variance matrix, It is the variance of the additive white Gaussian noise at the base station receiving antenna array. This is the user uplink power allocation matrix corresponding to the overlap phase. This is the user uplink power allocation matrix for the pure uplink communication phase, where diag(a) represents a diagonal matrix with elements of vector a as its diagonal elements, K represents the number of uplink users, and p k p′ represents the uplink transmit power of user k during the downlink sensing / uplink communication overlap phase. k s represents the uplink transmit power of user k during the pure uplink communication phase. l It is the sensing waveform of time slot l. s l The equivalent form of L d It is the time slot length of the sensed waveform, L u L is the time slot length of the uplink data. o It is the time slot length during the downlink sensing / uplink communication overlap phase.

[0062] (3) Based on the weighting coefficients, under the constraints of base station downlink sensing and user uplink transmit power, optimize the base station downlink sensing waveform and user uplink transmit power allocation with the goal of minimizing the weighted sum of the mean square error of sensing estimation and communication data detection.

[0063] In this step, based on the mean square error obtained in (2), the downlink sensing waveform S, the uplink communication power allocation matrix P and P′ are optimized, and the optimization problem is as follows:

[0064] The optimization objective is to minimize the weighted sum of mean squared errors.

[0065]

[0066] The constraints are:

[0067]

[0068] 0≤p k ≤P k k = 1, 2, ..., K

[0069] 0≤p′k ≤P k k = 1, 2, ..., K.

[0070] Where ρ represents the weighting coefficient, tr(·) is the trace of the matrix, and ||·||2 is the 2-norm of the vector. For x u1 The covariance matrix, For the echo phase, the equivalent uplink data vector is vec(·), which denotes vectorization. l P is the uplink data vector for time slot l. b P represents the base station transmit power budget. k This represents the uplink transmit power budget for user k.

[0071] To facilitate solving, the original optimization problem can be transformed into the following three sub-problems in this example:

[0072] Subproblem 1: With a fixed power allocation matrix P, solve for the downlink sensing waveform S.

[0073] The optimization objective is to minimize

[0074]

[0075] The constraints are:

[0076] in, and As an intermediate variable, The solution obtained in the t-th iteration The value, The solution obtained in the t-th iteration The value of .

[0077] Subproblem 2: Given a fixed downlink sensing waveform S, solve for the power allocation matrix P.

[0078] The optimization objective is to minimize

[0079]

[0080] The constraints are:

[0081] 0≤p k ≤P k k = 1, 2, ..., K

[0082] in, and It is an intermediate variable used to simplify formulas. The solution obtained in the t-th iteration The value of P (t) Let P be the value obtained from the t-th iteration.

[0083] Subproblem 3: Solving for the power allocation matrix P′

[0084] The optimization objective is to minimize

[0085] The constraints are:

[0086] 0≤p′ k ≤P k k = 1, 2, ..., K

[0087] in, P is an intermediate variable used to simplify the formula. ′(t) Let P' be the value obtained from the t-th iteration.

[0088] The three subproblems after the above transformation are all convex problems. Sub-optimization problem 1 and sub-optimization problem 2 can be solved alternately to obtain the optimal solutions for the downlink sensing waveform S and the power allocation matrix P when the iteration converges; the optimal solution for the power allocation matrix P′ can be obtained by iteratively solving sub-problem 3.

[0089] To verify the effectiveness of the present invention, a simulation experiment was conducted. The parameters involved in the simulation experiment are shown in the table below:

[0090] Table 1 Simulation Experiment Parameters

[0091] Number of base station transmit and receive antennas (4，8)、(12,16) Upstream users 3 Sensing waveform time slot length 10 Uplink data slot length 10 Overlapping portion time slot length 8 Weighting coefficients 0.7 Convergence threshold <![CDATA[10 -3 ]]> Large-scale fading in uplink communication -90dB Large-scale fading of sensing echo -100dB noise power -90dBm Base station downlink transmit power 35dBm

[0092] Figure 2 For comparison with simulation results, the simulation results show that the weighted mean square error of the joint design method gradually decreases with the increase of uplink transmit power, while the weighted mean square error of the independent design method first decreases and then increases with the increase of uplink transmit power. The weighted mean square error of the joint design method is significantly lower than that of the independent design method, and the gain of the joint design method is more significant under large antennas.

[0093] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

Claims

1. A method for integrating uplink communication and downlink sensing transmission, characterized in that, Includes the following steps: Step 1: Set the upper limit and weighting coefficient for the downlink sensing power of the base station and the uplink transmission power of the user respectively; Step 2: Based on the linear minimum mean square error (LMMSE) receiver for sensing parameter estimation and uplink communication data detection, obtain the mean square error for sensing estimation and communication data detection, respectively. Step 3: Based on the weighting coefficients, under the constraints of base station downlink sensing and user uplink transmit power, optimize the base station downlink sensing waveform and user uplink transmit power allocation with the goal of minimizing the weighted sum of the mean square error of sensing estimation and communication data detection; In step 3, based on the mean square error obtained in step 2, the following optimization problem is constructed to optimize the downlink sensing waveform. User uplink communication power allocation matrix and : The optimization objective is to minimize the weighted sum of mean squared errors. ； The constraints are: ； in, Indicates the weighting coefficient. The trace of the matrix, Let L be the L2 norm of the vector. for The covariance matrix, This is the equivalent uplink data vector for the echo phase. Vectorization is represented. For time slots The uplink data vector, This represents the base station transmit power budget. Indicates user Uplink transmit power budget; In step 3, the original optimization problem is transformed into three sub-optimization problems using the controlled minimization MM algorithm: Subproblem 1: Fixed power allocation matrix Solving the downlink sensing waveform ; The optimization objective is to minimize ； The constraints are: ; in, and As an intermediate variable, For the first The solution obtained in the second iteration The value, For the first The solution obtained in the second iteration The value; Sub-problem 2: Fixed downlink sensing waveform Solve for the power allocation matrix ; The optimization objective is to minimize ； The constraints are: ； in, and It is an intermediate variable used to simplify formulas. For the first The solution obtained in the second iteration The value, For the first The solution obtained in the second iteration The value; Subproblem 3: Solving the power allocation matrix ; The optimization objective is to minimize ； The constraints are: ； in, It is an intermediate variable used to simplify formulas. For the first The solution obtained in the second iteration The value of .

2. The integrated transmission method for uplink communication and downlink sensing according to claim 1, characterized in that, In step 2, the sensing parameters and uplink communication data are estimated using the following LMMSE receivers: Sensing receiver: ； Uplink communication receiver: ； in, Indicates a sensing receiver. Indicates time slot The uplink communication receiver, This indicates the conjugate transpose. Indicates matrix transpose. This represents finding the inverse of a matrix. Representation matrix and Kronecker's product, It is the target response vector The covariance matrix, It is a downlink sensing waveform. express The equivalent form, It is the user's uplink communication channel. The dimension is The identity matrix, The dimension is The identity matrix, Indicates as The identity matrix, This indicates the uplink signal in the overlapping part of downlink sensing / uplink communication. The variance matrix, It is the variance of the additive white Gaussian noise at the base station receiving antenna array. This is the user uplink power allocation matrix corresponding to the overlap phase. This is the user uplink power allocation matrix for the pure uplink communication phase. Represented by vector The elements are a diagonal matrix with diagonal elements. Indicates the number of upstream users. Indicates user Uplink transmit power during the downlink sensing / uplink communication overlap phase Indicates user Uplink transmit power during pure uplink communication phase It is a time slot The perceived waveform, express The equivalent form, It is the time slot length of the perceived waveform. It is the time slot length of the uplink data. It is the time slot length during the downlink sensing / uplink communication overlap phase.

3. The integrated transmission method for uplink communication and downlink sensing according to claim 1, characterized in that, In step 3, sub-optimization problem 1 and sub-optimization problem 2 are solved alternately, and the downlink sensing waveform is obtained when the iteration converges. and power allocation matrix The optimal solution; the power allocation matrix is ​​obtained by iteratively solving subproblem 3. The optimal solution.

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